Testing the evolutionary drivers of malaria parasite rhythms and their consequences for host–parasite interactions

Abstract Undertaking certain activities at the time of day that maximises fitness is assumed to explain the evolution of circadian clocks. Organisms often use daily environmental cues such as light and food availability to set the timing of their clocks. These cues may be the environmental rhythms that ultimately determine fitness, act as proxies for the timing of less tractable ultimate drivers, or are used simply to maintain internal synchrony. While many pathogens/parasites undertake rhythmic activities, both the proximate and ultimate drivers of their rhythms are poorly understood. Explaining the roles of rhythms in infections offers avenues for novel interventions to interfere with parasite fitness and reduce the severity and spread of disease. Here, we perturb several rhythms in the hosts of malaria parasites to investigate why parasites align their rhythmic replication to the host's feeding‐fasting rhythm. We manipulated host rhythms governed by light, food or both, and assessed the fitness implications for parasites, and the consequences for hosts, to test which host rhythms represent ultimate drivers of the parasite's rhythm. We found that alignment with the host's light‐driven rhythms did not affect parasite fitness metrics. In contrast, aligning with the timing of feeding‐fasting rhythms may be beneficial for the parasite, but only when the host possess a functional canonical circadian clock. Because parasites in clock‐disrupted hosts align with the host's feeding‐fasting rhythms and yet derive no apparent benefit, our results suggest cue(s) from host food act as a proxy rather than being a key selective driver of the parasite's rhythm. Alternatively, parasite rhythmicity may only be beneficial because it promotes synchrony between parasite cells and/or allows parasites to align to the biting rhythms of vectors. Our results also suggest that interventions can disrupt parasite rhythms by targeting the proxies or the selective factors driving them without impacting host health.


| INTRODUC TI ON
Circadian clocks allow organisms to adaptively align their rhythmic processes to predictable rhythmic changes in their environments (Jabbur et al., 2024;Yerushalmi & Green, 2009) that vary across the 24 hour day, including light, temperature, humidity and the rhythms of other organisms (Helm et al., 2017).Organisms evolve to maintain this alignment because environmental oscillations directly impact on fitness, provide timing information (i.e. as a 'Zeitgeber') for another fitness-impacting rhythm or both.Thus, the cues used to set the time of day of clocks may themselves directly impact fitness and be the evolutionary (ultimate) explanation for why an organism keeps time, or else provide (proximate) time of day information about correlated cyclical environmental opportunities and risks that impact fitness, or simply enable the coordination of internal processes regardless of the consequences of environmental alignment (Krittika & Yadav, 2020;Vaze & Sharma, 2013).Understanding how environmental rhythms shape rhythmic activities therefore requires disentangling their role as time cues from their role(s) as selective forces that drive the evolution and maintenance of rhythms (Hut & Beersma, 2011).
Assuming an organism's rhythms are adaptive (i.e.enhance fitness), this can be achieved by perturbing the alignment of an organism's rhythms to various environmental rhythms to ascertain which perturbations have detrimental fitness implications.
However, this is challenging because the links between environmental inputs, endogenous clocks, clock-controlled outputs and fitness returns are often unknown.Moreover, organisms often utilise multiple Zeitgebers (Harder & Oster, 2020), many of which interact, producing an array of rhythmic outputs which themselves may be regulated by multiple inputs, increasing the challenges of uncovering the fitness consequences of following particular environmental rhythms.
The use of cues to determine biological processes has long been studied in the context of phenotypic plasticity (reviewed in Schneider, 2022;Snell-Rood & Ehlman, 2021).Cues can be conceptually separated into those which are the 'selective (ultimate) drivers' of the evolution of a phenotype (i.e.environmental changes with direct fitness impacts) and cues which are 'proxies' (i.e.correlated with the selective driver but not of fitness significance per se).Importantly, proxies act as less reliable cues because they may not always perfectly reflect the state of selective drivers, but may be more convenient to measure.For example, the proxy versus selective driver concept has been used in evolutionary ecology to understand why prey animals use light or temperature as a correlate for predation risk (Miehls et al., 2013;Orrock et al., 2004;Suppa et al., 2021).Likewise, in a circadian context, the cues causing ('effecting') rhythmicity may be either the ultimate selective factors driving the evolution of phenotypic rhythmicity or correlative proxies.For example, the diel light cycle (day and night), which acts as a primary Zeitgeber for many organisms, may directly impact fitness (e.g. via photosynthetic potential or visual acuity), but also correlates with the activity of other organisms, generating the potential for a wide range of social, cooperative or exploitative interactions.
Reliance on a proximate time cue may also be a convenient way to ensure the separation or synchronisation of an organism's internal processes.In this context, the actual time of day processes are undertaken does not directly impact fitness, but temporally separating interfering processes (e.g.Chen et al., 2007) or maintaining homeostasis by synchronising several rhythmic processes can be beneficial (Vaze & Sharma, 2013).
Ascertaining to what extent the proximate cues that effect rhythmic activities are also ultimate selective drivers offers a novel approach to managing and manipulating interactions between organisms.For example, rhythms in the activities of parasites (including pathogens) underpin their virulence and transmission, whereas rhythms in host immune responses and feeding patterns can determine the severity and outcome of infections (Rijo-Ferreira & Takahashi, 2022).Understanding the links between responses to rhythmic cues and fitness, for both parasites and hosts, are fundamental steps towards reducing the benefits parasites garner from their rhythms and/or harnessing host rhythms to control infections (Hunter et al., 2022;Westwood et al., 2019).For example, Trypanosoma brucei, which causes sleeping sickness, uses the host's body temperature rhythm to entrain its circadian clock which, in turn, coordinates the expression of its metabolism-related genes (Rijo-Ferreira et al., 2017).Intuition suggests this allows the parasite to coordinate its own feeding with that of its host, but whether the host's feeding-fasting rhythms are the ultimate driver remains untested.Rhythmic replication by malaria parasites also aligns with the host's feeding-fasting rhythms.Malaria (Plasmodium) parasites are famously rhythmic; completing cycles of replication with the host's red blood cells (RBCs) at 24, 48 or 72 hourly intervals, depending on the species (Dos Santos et al., 2021;Garcia et al., 2001;Mideo et al., 2013).Each replication cycle-termed the intraerythrocytic developmental cycle (IDC)-culminates in synchronous bursting to release progeny that initiate the subsequent round of RBC invasions and causing the periodic fever that characterises malaria infection (Gazzinelli et al., 2014).
In the rodent malaria model Plasmodium chabaudi, the timing of transitions between IDC stages aligns to diel (~24 h) host rhythms associated with feeding-fasting.This alignment occurs even when the host's light-dark cycle is in an opposing phase or when the host's canonical circadian clock machinery (transcription-translation feedback loop; TTFL) is disrupted (Hirako et al., 2018;O'Donnell et al., 2020O'Donnell et al., , 2022;;Prior et al., 2018).The IDC completes near the end of the feeding window, which is night-time for nocturnally active rodent hosts.The IDC rhythm is at least partly 'endogenous', that is, under the control of parasite genes, and appears to free run with a period of about 24 h in the absence of entrainment (Prior et al., 2020;Rijo-Ferreira et al., 2020;Smith et al., 2020;Subudhi et al., 2020).Temperature compensation, another key criterion of a circadian clock, is yet to be tested for (Dunlap et al., 2004).If the timing of the IDC is perturbed relative to the phase of the host's feeding-fasting rhythm, the IDC speeds up by 2-3 h per cycle until realigned to host rhythms (O'Donnell et al., 2022).The timing (phase) of the IDC rhythm is important for parasite fitness; it maximises within-host replication, results in transmission stages (gametocytes) being mature and at their most infectious during the night time when mosquito vectors forage for blood, and confers tolerance to antimalarial drugs (O'Donnell et al., 2011(O'Donnell et al., , 2022;;Owolabi et al., 2021;Pigeault et al., 2018;Schneider et al., 2018).
It is not known whether aligning to the IDC the host's feedingfasting schedule, or downstream rhythms, provides a direct fitness benefit to parasites (i.e. a selective driver), and/or if a feeding-fasting rhythm simply provides a convenient timing cue (or series of cues) for other environmental rhythms that impact parasite fitness.If rhythmic feeding/fasting is only a proxy, it is likely a cue for lightentrained rhythmic host processes, which are mediated in different tissues by the TTFL clock machinery (Astiz et al., 2019;Zhang et al., 2020).For example, rhythms in immune responses are often correlated with the timing of feeding-fasting.Previous studies suggest that innate immune rhythms do not impose the IDC rhythm by preferentially killing misaligned parasites, though immune cell metabolism may exacerbate the need for the IDC to be aligned with blood nutrient rhythms (Cabral et al., 2022;Hirako et al., 2018;Hunter et al., 2022;Prior et al., 2020).Aligning with feeding-fasting rhythms could directly improve parasite fitness because the host's digestion of food regulates when parasites have access to essential nutrients they cannot scavenge from haemoglobin.These resources include vitamins B1 and B5, folate, purines and the amino acid isoleucine (which is absent from human haemoglobin and uniquely rare in murine haemoglobin), that are required by later IDC stages for biogenesis (Skene et al., 2018).Indeed, recent work reveals that as well as being an essential rhythmically available resource, blood isoleucine concentration also fulfils criteria of a time cue used by P. chabaudi to set its IDC schedule (Prior et al., 2021).This suggests that isoleucine could be both a proximate cue and a selective driver; parasites respond to isoleucine rhythms because isoleucine availability regulates replication, and the isoleucine rhythm is in phase with other nutrients that are only, or most easily, acquired from the host's food.Whether it is also beneficial to align gametocyte development with rhythmic nutrients is not known, and feeding-fasting rhythms may alternatively, or additionally, be a proxy for vector activity rhythms (i.e. because both are correlated with the day-night cycle).Finally, it is possible that irrespective of external rhythms, parasites might use a host feeding-fasting rhythm simply to synchronise development or coordinate life-history decisions (e.g.cellcell communication involved in reproductive investment decisions (Schneider & Reece, 2021)) across individual parasite cells within each IDC cohort.However, theory also predicts that if the IDC is too tightly synchronised across an IDC cohort, parasites inadvertently compete for resources (Greischar et al., 2014;Owolabi et al., 2024).
Here, we investigate to what extent the host's light-driven and feeding-fasting rhythms are ultimate selective drivers of the IDC rhythm by manipulating host rhythms and assessing the impacts on parasite performance throughout the whole duration of infection.Specifically, we ask three key questions (denoted throughout as Q): (Q1) Do parasites derive ultimate fitness benefits from aligning to host rhythms entrained by the light-dark cycle?; (Q2) Do parasites derive greater fitness benefits when rhythmic hosts have typically rhythmic feeding-fasting?; and (Q3) Do parasites benefit from specifically aligning to feeding-fasting rhythms in TTFL-disrupted hosts with no other discernible rhythms?We consider both key fitness components of parasites: their within-host survival and betweenhost transmission.We also investigated whether the severity of disease symptoms experienced by hosts depends on how their parasites perform, their own rhythms and their access to food.Determining how host rhythms contribute to their own fitness and that of malaria parasites, is timely and important given that plasticity in the IDC schedule helps parasites tolerate antimalarial drugs (Owolabi et al., 2021;Teuscher et al., 2010).Beyond malaria, understanding why the timing and synchrony of parasite replication are connected to the daily rhythms of hosts may make drug treatment and other interventions more effective and less toxic to patients.

| Hosts and parasites
We used both C57BL/6J (WT) wild-type and Per1/2-null clockdisrupted mice backcrossed onto a C57BL/6J background for over 10 generations (O'Donnell et al., 2020).All experimental mice were females, approximately 11 weeks old at the start of the experiment and had been group housed at ~20°C, 60% RH, with a 12:12 Light: Dark regime (lights on 0800-2000; all times in UCT + 1), ad libitum access to food (RM3 pellets, 801,700, SDS, UK) and unrestricted access to drinking water supplemented with 0.05% para-aminobenzoic acid (Jacobs, 1964).Two weeks before infection, we singly housed mice and randomly allocated them to treatment groups (n = 5 per group) to begin their acclimatation to experimental photoschedules and feeding treatments, which we maintained for the duration of the experiment (see Experimental design for infection and treatment timings).All wild-type mice remained in LD 12:12, in which they exhibit nocturnal activity and foraging via the mammalian circadian system; the TTFL clock oscillates in cells throughout the body and keeps time via entrainment to light (Finger & Kramer, 2021;Partch et al., 2014).In contrast, Per1/2-null mice were transferred to constant darkness in which they are behaviourally arrhythmic because null versions of the Per1 and Per2 clock genes disrupt the canonical TTFL machinery and no light-dark cues are available to invoke direct ('masking') responses (Bae et al., 2001;Maywood et al., 2014;O'Donnell et al., 2020).Parasites are cryopreserved in liquid nitrogen and expended in donor mice before initiating experimental infections (de Roode et al., 2004).All mice received an intravenous infection of 10 5 red blood cells infected with P. chabaudi (genotype DK) at the ring stage, an early stage in the IDC (Prior et al., 2020).DK parasites cause relatively mild infections, which minimises off-target effects of sickness altering host rhythms and confounding parasite performance (Prior et al., 2019).P. chabaudi, like all the rodent malarias, was collected from countries across central Africa where hosts, which includes Mus musculus, experience equatorial lightdark schedules (12:12 LD;Carter, 2022;Pattaradilokrat et al., 2022).

| Experimental design
We used five treatment groups to compare parasite and host performance metrics within pairs of groups that enabled three questions (Q) to be asked about the consequences of infecting hosts with different kinds of rhythms for parasite performance and infection severity (Figure 1).Our approach aimed to decouple the time cues available to parasites from different host rhythms in manners that minimise confounding impacts of forcing parasites to alter IDC schedule which would occur if treatments involved misaligning the timing of the IDC to feeding-fasting rhythms.The treatments were as follows: (i) 'WT-AL' (Wild Type-ad libitum), wild-type hosts in LD with food constantly available.The feeding-fasting and light-entrained rhythms of these mice are aligned because they follow their natural patterns of nocturnal behaviour and undertake the bulk of their foraging in the dark.
(ii) 'WT-DF' (Wild Type-Dark Fed), wild-type hosts in LD with time-restricted feeding (hereafter "TRF") in which food was only available during the dark period of each daily cycle.The  (Prior et al., 2018); and (d) the parasite's IDC schedule, illustrated by the timing of bursting to release progeny (O'Donnell et al., 2020;Prior et al., 2021).Note, the IDC rhythm can be decoupled from rhythms entrained by the host's light-dark cycle, but consistently reschedules to the host's feeding-fasting rhythm, precluding direct assessment of the fitness impacts of feeding-fasting associated rhythms.The lower four rows show the pairwise comparisons between treatments (solid boxes) used to test each of the following key questions (Q), where −/+ denotes the treatments in which parasites are expected to perform worse/better within each pair.For the three questions, respectively, we compare hosts in which: (Q1) The timing of feeding-fasting rhythms is aligned (WT-DF) or misaligned (12 h out of phase; WT-LF) to light-dark rhythms.If feedingfasting cues are used as a proxy for light-entrained rhythms that impact on parasite fitness, parasites will perform worse when aligned to feeding-fasting rhythms that are decoupled from light-entrained rhythms (WT-LF).(Q2) Feeding is restricted to 12 h windows (WT-DF) or available throughout the day (WT-AL).Ad lib fed hosts spread their food intake around a peak in the dark phase (O'Donnell et al., 2020), thus, feeding cues may peak at similar times in WT-AL and WT-DF hosts but ad lib hosts take in food over a longer window that includes dusk and dawn.If the IDC rhythm represents a balance between the benefits of timing to align with nutrient availability versus the costs of extreme synchrony causing competition, this constraint will be ameliorated in WT-AL hosts who can spread their feeding out and so we predict that parasites will perform better in WT-AL hosts.(Q3) TTFL clocks are disrupted and feeding rhythms are either attenuated (Per1/2-AL) or experimentally imposed (Per1/2-RF).Parasites align to feeding-fasting rhythms even in clock-disrupted hosts (via TRF).If parasites benefit from non-TTFL-mediated aspects of rhythmic host feeding (Greenwell et al., 2019;O'Donnell et al., 2020) or from intrinsic benefits of synchrony, and these benefits outweigh potential costs, parasites will also perform better in Per1/2-RF than Per1/2-AL hosts.Finally, we also predicted that in groups in which parasites performed better, hosts will experience more severe infection symptoms, but that hosts with constant access to food (WT-AL and Per1/2-AL) cope better with infection.
typically consume the same amount of food per day as under ad libitum conditions, even when the feeding window is less than 12 h per day (e.g.Froy et al., 2006;Hatori et al., 2012), at least when given 2 weeks to adjust to TRF, as we allow for in the present study (e.g.Hiroshige et al., 1983).
(iii) 'WT-LF' (Wild Type-Light Fed), wild-type hosts in LD with time-restricted feeding in which food was only available during the light period of each daily cycle.By inverting the timing of food availability relative to the light-dark schedule, the parasite's IDC continues to be aligned to the host's feeding-fasting rhythms but becomes misaligned to the host's light-entrained rhythms.
(iv) 'Per1/2-RF' (Per1/2-null-Restricted Fed), Per1/2-null hosts in DD with time-restricted feeding in which food was only available during a 12-h window each day (2000-0800).These hosts exhibit experimentally imposed rhythmicity in enough processes related to the digestion of food, metabolism and fasting to schedule the IDC (O'Donnell et al., 2020), but with no influence of TTFL-driven clocks.
Infections were initiated 2 weeks after mice had acclimated to their experimental photoschedules and feeding treatments.To ensure that all infections were initiated with parasites aligned to the feeding-fasting schedule of their recipient host, we infected using parasites harvested from donor hosts housed in two different LD 12:12 photoschedules.Specifically, parasites were harvested from the end of the respective donor dark phases, to ensure rings stages predominated, at 0830 to infect the WT-AL, WT-DF, Per1/2-RF and Per1/2-AL treatments, and at 2030 on the same day to infect the WT-LF treatment.Throughout acclimation and the experiment, we checked food twice daily to ensure a constant supply to ad libitum fed mice (WT-AL, Per1/2-AL) and swept cages for stray pellets when food was removed from TRF mice (Per1/2-RF, WT-DF, WT-LF).

| Sampling and data collection
Previous studies have attempted to assess fitness impacts from only a few IDCs at the start of infections (e.g.O'Donnell et al., 2013) and rarely considered gametocytes, but the selective advantage of the IDC may vary throughout infection and across parasite life cycle forms (Prior et al., 2020).To overcome these limitations, we monitored infections over 17 days, which includes the establishment, expansion and decline of the burden of asexually replicating (i.e.IDC stages) parasites and captures the bulk of gametocyte production to assess transmission potential.Specifically, we assessed parasite performance in terms of overall parasite and gametocyte density dynamics, and infection severity in terms of anaemia and weight loss.
We sampled mice daily from day 3 to day 17 post infection (PI) at 0830 for the Per1/2-RF, Per1/2-AL, WT-AL and WT-DF treatments, and at 2030 for the WT-LF treatment, to ensure the age of infection (in hours) was consistent across treatments.Day-to-day dynamics are appropriate because the densities of asexuals and gametocytes vary at least an order of magnitude more across days than within a day (O'Donnell et al., 2011(O'Donnell et al., , 2021;;Westwood et al., 2020).Four mice were euthanised due to reaching the humane endpoints of infection (indicating they were at risk of not recovering) in the following treatments (at days PI): WT-DF (10), Per1/2-RF (8), Per1/2-AL (9, 9).At each sampling point, we weighed mice and collected blood samples (2 μL for RBC density, 5 μL for total parasite density and 10 μL for gametocyte density).
We measured RBC density using a particle counter (Beckman Coulter Z2).For total parasite density, we mixed 5 μL blood samples with 150 μL citrate saline upon collection and (after centrifuging and discarding the plasma supernatant) extracted DNA for qPCR.
For gametocyte density, we mixed 10 μL blood samples with 20 μL RNAlater® upon collection and extracted RNA for RT-qPCR.We followed extraction and qPCR protocols targeting the CG2 gene (PCHAS_0620900) as detailed elsewhere (Owolabi et al., 2021;Schneider et al., 2015).Notably, since the CG2 gene is expressed only in gametocytes (Wargo et al., 2007), CG2 cDNA quantifies the number of gametocytes, whereas CG2 DNA quantifies the total number of asexually replicating stages and gametocytes.All animal procedures were conducted in accordance with UK Home Office (license no.PP8390310) regulations and approved by an ethics board at the University of Edinburgh.

| Data analysis
We quantified metrics for parasite fitness using parasite density as a measure of in-host survival, and gametocyte density as a measure of between-host transmission potential.For each of these density metrics, we analysed: (i) the dynamics of log-transformed density throughout the whole infection, using day PI as a factor (since density is non-linear) and random intercepts for each host ID; (ii) peak density, defined as the highest log-transformed density observed for each host (or each infection stage, i.e. early or late, per host for gametocytes) and (iii) overall density, defined as the cumulative number of parasites observed throughout infection and calculated only from mice that survived the entire experiment.For parasite density, two samples were defective and excluded (specifically, the samples did not amplify during successive PCRs, suggesting collection or extraction error).For gametocytes, P. chabaudi exhibits two peaks during infections which we refer to as the 'early' and 'late' windows (before or after day 10PI), and we considered each window of gametocyte production separately.We excluded Per1/2-RF infections from all gametocyte analyses due to loss of the RNA samples for this group (specifically, samples from 4 of the 5 infections were implausibly high on day 6PI, likely due to a collection error, which invalidated analyses for this treatment), meaning it was not possible to test Q3 in relation to transmission potential.We quantified weight loss and anaemia as the difference between weights and RBC densities on day 3PI and at their respective troughs.
All analyses were conducted using R version 4.0.0 or later (R Core Team, 2021).We took a two-stage approach to the analysis of each metric.First, we produced a separate linear model or linear mixed model (using the lme4 package v 1.1.32;Bates et al., 2015) set for each metric as a response variable and checked assumptions using the DHARMa package (v 0.3.3.0;Hartig, 2020).We then compared whether each of these models were more parsimonious than the respective null models (i.e. with no treatment term).Parsimony was determined using AICc (corrected Aikake's Information Criterion), a measure appropriate for model comparisons at both small and large sample sizes (see Symounds & Moussalli, 2011).Second, for those models where metrics varied detectably between treatments (without interaction), we conducted pairwise comparisons corresponding to our three main questions (Figure 1).We configured the models with the reference group (model intercept) as the WT-DF treatment for contrasts with WT-LF (Q1) and WT-AL (Q2), and with the reference group as the Per1/2-AL treatment for contrast with Per1/2-RF (Q3), and examined individual contrasts between these levels.For linear mixed models, we estimated p-values via Satterthwaite's degrees of freedom method using the 'lmerTest' package (v 3.1.3;Kuznetsova et al., 2017).We estimated effect sizes and confidence intervals for figures using nonparametric bootstrap resampling via the dabestr package (v 0.3.0;Ho et al., 2019).

| Parasite density
For the dynamics of parasite density, the most parsimonious model only included treatment and day PI (model weight = 0.80, delta AICc of all other models >3.08, see Table S1), implying that the dynamics of all groups followed a similar trajectory over time but varied in magnitude (Figure 2a,b).Pairwise comparisons within this model only revealed a difference in the comparison for Q1, but in the opposite direction to our prediction.Specifically, relative to WT-DF infections, the density was 43% higher in WT-LF infections (Q1;   S1).Pairwise comparisons within this model only revealed a difference in the comparison for Q2 (Figure 2c).Specifically, relative to WT-DF infections, the peak did not differ in WT-LF infections (Q1; t = 1.3, p = 0.22), but was 51.4% higher in WT-AL infections (Q2; t = 2.3, p = 0.032; coefficient of log density = 0.41, 95% CI = 0.04-0.S1).

| Gametocyte density
For gametocyte density dynamics, the most parsimonious model included treatment, day PI and a treatment × day PI interaction (model weight >0.99, delta AICc of all other models >34; see Table S2).
Gametocyte density dynamics followed similar qualitative patterns across the treatment groups (Figure 3a but generally lower in the late window (post 10 days PI), relative to parasites in WT-DF hosts (Q1, see Table S3).Gametocyte densities in WT-AL infections were more similar to WT-DF infections throughout, but with a trend for the peak of the late window to occur sooner (Q2).
The most parsimonious model for the peak of the late gametocyte window did not include treatment (null model weight = 0.98, delta AICc of model with treatment = 7.45).

| DISCUSS ION
We investigated to what extent light-and feeding-driven host rhythms are the ultimate drivers of the rhythmic replication of malaria parasites, by assessing the impacts of different combinations of host rhythms on the within-host survival and transmission potential of parasites.We also tested how perturbations to host rhythms affected the severity of infections.Few of the specific focal group comparisons underpinning our questions revealed significant differences even though we detected differences between treatment groups for most metrics (6 of 9 model sets), suggesting the analyses had sufficient power.Overall, we reveal more treatment group differences in total parasite than gametocyte densities, and that body weight is more sensitive to host rhythm perturbations than anaemia.Specifically, total parasite dynamics followed the same qualitative patterns across treatments, but parasites in WT-LF hosts were able to maintain higher post-peak densities than those in WT-DF hosts (Q1) and WT-AL parasites achieved a higher peak than those in WT-DF hosts (Q2).Likewise, gametocyte density dynamics followed qualitatively similar patterns across treatment groups, but with trends for parasites in WT-DF hosts to have a lower peak in the early window than WT-LF infections (Q1) and delayed peak in the late window compared to WT-AL infections (Q2), and gametocyte density dropped faster after the late peak in WT-DF than WT-AL infections (Q2).Finally, while red blood cell loss did not differ between the treatment groups, hosts that had the most access to food (WT-AL) lost the least weight (Q2), but internal desynchrony (of light-and fooddriven rhythms) did not exacerbate virulence (Q1).Taken together, our results imply that: (Q1) the ultimate driver(s) of parasite rhythms are unlikely to be based on within-host processes driven directly by the light-dark cycle, since parasites (whose IDC rhythm follows host feeding-fasting) did not perform better in any fitness metric when matching the host's light-driven rhythms; (Q2) parasites benefit when the host feeds in a spread-out-but-rhythmic pattern, since peak parasite density was higher in hosts with a more widely distributed feeding window than hosts with extrinsically imposed feeding restricted to the night time; and (Q3) whilst imposing rhythmic feeding on clock disrupted hosts is sufficient to generate the IDC rhythm (O'Donnell et al., 2020), this has no apparent ultimate benefit in the absence of the host's TTFL clock machinery, since parasites did not differ in any metric between clock-disrupted hosts with or without rhythmic feeding.
If light-entrained host rhythms are an important ultimate driver of the IDC rhythm, we predicted that parasites would suffer when the IDC is misaligned to light-entrained rhythms (WT-LF).This was not the case, and WT-LF parasites may even perform slightly better in terms of transmission potential at some points during infections.The outputs of light-entrained host clocks usually include the sleep-wake cycle, which is involved in the deployment of T cells (Besedovsky et al., 2012), some components of innate and adaptive immunity (Carvalho Cabral et al., 2022), and some metabolic and homeostatic processes (e.g.Huang et al., 2011), suggesting rhythms in these processes have little impact on parasite fitness.Instead, the most parsimonious (but non-mutually exclusive) conclusions of the result of Q1 are that parasites follow host feeding-fasting rhythms to derive benefits from aligning their development with (i) rhythmicity in blood nutrients derived from food digestion (Prior et al., 2021), (ii) rhythms that more closely follow the phase of host feeding than light cues, including blood oxygen tension (Zhang et al., 2021), body temperature (in small mammals; Abrams & Hammel, 1964) and some immune rhythms (Chen et al., 2022); or (iii) vector activity rhythms, which correlate with nocturnal host feeding-fasting.
Aligning with vector rhythms benefits diverse Plasmodium species (Pigeault et al., 2018;Schneider et al., 2018), but the fitness consequences of other within-host physiological rhythms remain untested.Alternatively, there may be (iv) no benefits of aligning with host or vector rhythms and parasites use host feeding-fasting cues simply to ensure an optimal level of synchrony in the IDC.Theory predicts that the optimal level of IDC synchrony is a trade-off between the benefits of being synchronous enough to exploit timedependent nutrients from the host's food and the costs of extreme synchrony causing inadvertent competition between parasite cells that are close kin (Greischar et al., 2014;Owolabi, 2023).Further teasing apart scenarios i-iv and ascertaining their relative importance is empirically very challenging.Confronting parasites with different kinds of rhythms is possible thanks to the tools available for lab mouse models (including conditional clock disruptions) but off-target effects of genetic manipulations can introduce confounders.Furthermore, experiments must avoid confounding experimental treatments with the costs incurred by parasites altering the IDC rhythm if their alignment to feeding-fasting rhythms is perturbed.
Given the likely reliance of parasites on rhythmic host resources and the potential for high synchrony to be costly, we predicted that parasites derive greater fitness benefits when hosts have ad libitum access to food throughout, compared to hosts whose feeding was restricted to the 12 h dark phase (albeit with a similar peak feeding time (Q2)).This prediction was supported by parasites in WT-AL hosts achieving a 50% higher peak parasite density.TRF does not typically reduce the amount of food that mice consume per day, even when the window of food availability is much shorter than in our experiment (e.g. 3 h; Froy et al., 2006), suggesting that the duration of the feeding window is the key difference between these groups.Furthermore, TRF has broad metabolic impacts, including altering nutrient absorption and temporal expression patterns of various metabolic genes compared to ad libitum fed mice (Gallop et al., 2023), suggesting parasites experience significantly different conditions in WT-AL hosts.Spreading out foraging benefited hosts too, ameliorating weight loss despite higher parasite densities.In contrast, WT-DF and WT-AL hosts experience the same degree of anaemia which is surprising since weight loss and anaemia are usually correlated (Timms et al., 2001).This suggests that WT-DF hosts experience a specific difficulty in dealing with malaria-induced appetite reduction when the foraging window is already limited.This is intriguing, since rodent feeding rhythms plasticly respond to environmental and seasonal changes (Caravaggi et al., 2018;Cohen et al., 2009;Tachinardi et al., 2015) and wild-house mice have very varied diets (Singleton & Krebs, 2007).Thus, whether WT-DF or WT-AL feeding rhythms-and the costs of infection-better reflect those of wild rodent hosts is likely to depend on the relative consumption of stored food (Singleton & Krebs, 2007) and rhythmically available forage (e.g.due to ambient temperatures and energetic efficiency; Hut et al., 2011) in their evolutionary history.
Our third question (Q3) considered whether feeding-fasting rhythms act as an ultimate driver as well as a proximate cue for the IDC rhythm (Prior et al., 2021).Directly testing this hypothesis is not possible since it requires misaligning parasites to host feedingfasting rhythms and it is not possible to prevent parasites from rescheduling the IDC to realign to feeding-fasting rhythms (which can occur within 5 cycles, O'Donnell et al., 2011O'Donnell et al., , 2020O'Donnell et al., , 2022)).Thus, our finding that parasite performance did not differ between infections in clock-disrupted hosts, regardless of whether a feeding rhythm was imposed via TRF (i.e.Per1/2-AL vs. Per1/2-RF) has several possible (non-mutually exclusive) interpretations.First, host feeding-fasting rhythms are a proximate but not ultimate driver of the IDC rhythm, allowing parasites to align to other (typically correlated) rhythms (discussed in Q1, above) or to achieve the optimal level of synchrony.
However, we propose that the intrinsic benefits hypothesis is least likely to explain the IDC rhythm because it predicts that synchrony is adaptive regardless of resource availability (i.e.Per1/2-RF parasites should have outperformed Per1/2-AL parasites, which they did not).Second, there are benefits of aligning specifically with feedingfasting rhythms but these are offset by other costs.For example, parasites in Per1/2-RF hosts may benefit from aligning IDC stages with the availability of the nutrients they need but the TRF duration might have increased synchrony to a costly level (analogous to why parasites in WT-AL hosts may perform better than those in WT-DF hosts).Alternatively, parasites in Per1/2-AL hosts may benefit from avoiding nutrient limitation but may suffer from a loss of intrinsic benefits (if synchrony alone is adaptive).These different costs and benefits may coincidently result in no net differences between treatment groups.Third, the ultimate driver of the IDC rhythm may be a host rhythm that requires input from both feedingfasting rhythms and the TTFL clock, which was not experienced by parasites in Per1/2-RF hosts.This scenario suggests that only a TTFL-mediated component of feeding-fasting rhythms is an ultimate driver for IDC rhythms and that time cue(s) such as isoleucine (which can be rhythmic in the absence of the TTFL) function as a proxy.
Mechanistically, this fits with recent models of peripheral rhythms in mammals, whereby feeding drives some downstream physiological outputs directly, but others are mediated by TTFL clocks in the liver and other peripheral organs, as well as the central pacemaker in the SCN (Zhang et al., 2020).For example, both rhythmic feeding and a functioning TTFL clock are needed to generate rhythmic gene expression of some metabolic genes involved in lipogenesis and glycogenesis in the liver (Greenwell et al., 2019;Vollmers et al., 2009), and so these or similar metabolites may be candidate ultimate drivers of parasite rhythmicity.

| CON CLUS IONS
Our study complements the increasing body of work focussing on 'how' Plasmodium parasites set the schedule of the IDC rhythm by asking 'why' this rhythm is adaptive.Identifying selective drivers is a challenge for a trait that does not have the benefit of, for example, the well-established theoretical literature on adaptation that the field of life-history evolution benefits from.Our results suggest purely light-driven rhythmic host processes are not the selective, ultimate, driver of parasite rhythmicity.Nonetheless, further experiments are necessary to verify this deduction.Further work is also required to explore the new hypothesis we propose; that feeding-fasting rhythms are a selective driver but only when mediated by TTFL clocks.Testing this, along with the ultimate roles of other rhythms, such as oxygen tension and vector activity, would be facilitated by knowing the molecular mechanism(s) that underpin the timing and synchrony of the IDC.For example, blocking parasites' ability to sense time or alter the IDC schedule would stabilise misalignment to any host rhythm, enabling fitness consequences to be directly assessed.While our experimental design improves on previous studies of fitness consequences by considering whole infections rather than a few IDCs, the adaptive value of rhythms in other taxa have been most clearly demonstrated in stressful conditions such as competition (Dodd et al., 2005;Fleury et al., 2000;Jabbur et al., 2024;Ouyang et al., 1998).Within-host competition is frequently experienced by Plasmodium species and experimentally tractable, as is manipulating overall resource availability via the diet of hosts.Overall, the relatively modest impacts of host rhythm manipulations on within-host parasite fitness metrics emphasises the role of rhythmic transmission opportunities as a putative ultimate driver, as proposed to explain rhythms in other parasite taxa (e.g. feeding-fasting and light-entrained TTFL rhythms of these mice are aligned and they differ from the WT-AL group because they cannot eat between dawn and dusk.Mice and rats under TRF F I G U R E 1 Characteristics of treatment groups and rationale for study design.The upper four rows show: (a) Photoschedule; either light-dark cycles (LD12:12, white and black bars) or constant darkness (DD, black bars).Feeding regime; time-restricted feeding limited to 12 hours per day (RF, 1 cheese) or ad libitum (AL, 2 cheeses).Cartoon waveforms illustrate typical patterns for: (b) light-driven TTFL rhythms governed by the SCN, illustrated by locomotor activity (O'Donnell et al., 2020; Prior et al., 2018); (c) feeding-fasting and downstream peripheral rhythms, illustrated by feeding (O'Donnell et al., 2020) as well body temperature t = 2.4, df = 341, p = 0.016, coefficient of log density = 0.36, 95% CI 0.21-0.51),but did not differ in WT-AL infections (Q2; t = −0.94,F I G U R E 2 Parasite density metrics.Density dynamics (means ± SE) for (a) infections of wild-type hosts and (b) infections of Per1/2null hosts from 3 to 17 days post-infection (PI), in which the colours in C correspond to the groups in all figures.Peak parasite densities (c) and cumulative densities (d) by treatment (upper subplot within each panel), along with effect sizes (lower subplot within each panel).Specifically, upper subplots depict (log) peak or cumulative parasite densities per host (coloured points) along with mean ± SE per treatment (white dots and black error bars).Lower subplots depict the effect sizes (mean ± 95% CI difference; white circles and error bars coloured by treatment) of each focal between-treatment comparison (defined by the key questions) of (log) peak or cumulative parasite density.For each comparison, the reference treatment is shown as a point and dotted line (Q1&2, WT-DF, teal; Q3, Per1/2-AL, pink), and the CIs are derived from nonparametric bootstrap resampling (distribution depicted alongside each error bar).The prediction for each key question is shown below in panel (d).
p = 0.35) and relative to Per1/2-AL infections, Per1/2-RF infections did not differ (Q3; t = 0.66, p = 0.51).The second most supported model also included a treatment × day PI interaction term (model weight 0.17) which we investigate further by comparing peak and cumulative densities.The mean (±SE) peak parasite density across treatments was 2.24 × 10 9 (±1.83 × 10 8 ) cells per ml of blood.The most parsimonious model explaining peak density included a treatment term (model weight = 0.99, delta AICc of null model = 9.4; Table 79) and relative to Per1/2-AL infections, the peak was not different in Per1/2-RF infections (Q3; t = 0.59, p = 0.56).The mean (±SE) cumulative parasite density across treatments was 7.98 × 10 9 (±4.34 × 10 8 ) cells per ml of blood.The most parsimonious model explaining cumulative density included the treatment term (model weight = 0.93, delta AICc of null model = 5.1; Table ,b), with WT-LF infections sustaining higher densities over the early window (pre 10 days PI), F I G U R E 3 Gametocyte density metrics.Density dynamics (means ± SE) for (a) infections of wild-type hosts from 3 to 17 days postinfection (PI), in which the colours in (c) correspond to the groups in all figures.Peak gametocyte densities of the early (b) and late (c) windows, and cumulative densities (d) by treatment (upper subplot within each panel), along with effect sizes (lower subplot within each panel).Specifically, upper subplots depict (log) peak or cumulative gametocyte densities per host (coloured points) along with mean ± SE per treatment (white dots and black error bars).Lower subplots depict the effect sizes (mean ± 95% CI difference; white circles and error bars coloured by treatment) of each focal between-treatment comparison (defined by the key questions) of (log) peak or cumulative parasite density.For each comparison, the reference treatment, WT-DF, is shown as a teal point and dotted line, and the CIs are derived from nonparametric bootstrap resampling (distribution depicted alongside each error bar).The prediction for each key question is shown below in panels (b, d).

F
I G U R E 4 Parasite virulence metrics.Host weight dynamics (means ± SE) for (a) infections of wild-type hosts and (b) infections of Per1/2-null hosts from 3 to 17 days post-infection (PI), in which the colours in (c) correspond to the groups in all figures.Maximum weight loss per mouse (weight at Day 3 PI minus lowest weight) is shown by treatment (c upper subplot), along with effect sizes (c lower subplot).Host red blood cell (RBC) count dynamics (±SE) for (d) infections of wild-type hosts and (e) infections of Per1/2-null hosts from 3 to 17 days PI.Maximum RBC loss (in 10 9 cells per mL) per mouse (RBC count at Day 3 PI minus lowest RBC count) is shown by treatment (f upper subplot), along with effect sizes (f lower subplot).Specifically, upper subplots in (c) and (f) depict weight or RBC loss, respectively, per host (coloured points) along with mean ± SE per treatment (white dots and black error bars).Lower subplots depict the effect sizes (mean ± 95% CI difference; white circles and error bars coloured by treatment) of each focal between-treatment comparison (defined by the key questions) of weight or RBC loss respectively.For each comparison, the relevant reference treatment is shown as a point and dotted line (Q1&2, WT-DF, teal; Q3, Per1/2-AL, pink), and the CIs are derived from nonparametric bootstrap resampling (distribution depicted alongside each error bar).The prediction for each key question is shown below in panels (c and f).